The purpose of data clustering algorithm is to form clusters (groups) of data points such that there is high intra-cluster and low inter-cluster similarity. There are different types of clustering ...

In the fast-paced field of High Performance Computing (HPC), the convergence of Artificial Intelligence (AI) and Big Data Analysis signals a groundbreaking ...

PhoSim simulates the atmosphere, including air turbulence, as well as distortions from the shape of the telescope’s mirrors and the electrical properties of the sensors. The photons are propagated ...

Hongwei Li’s team from China University of Geosciences published a research article titled “scSCC: A swapped contrastive ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

VanderSchaaf, C. L. (2025) Predictive Ability of Volume-Basal Area Ratios (VBARs) in Minnesota, USA Forests to Estimate ...

A novel clustering technique based on the projection onto convex set (POCS) method, called POCS-based clustering algorithm, is proposed in this paper. The proposed POCS-based clustering algorithm ...

Explore SET 100 historical data, featuring daily prices, open, high, low, volume, and changes. Analyze trends, all-time highs, historical returns, and more.

As the president launches a trade war, follow the latest on tariffs and executive orders ...

Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

We take a deep dive into the inner workings of the wildly popular AI chatbot, ChatGPT. If you want to know how its generative ...

AI crafts glider shapes modeled on the motion of marine life forms MIT’s AI model enhances underwater gliders' lift-to-drag ...